Background: Radiation-induced dermatitis (RID) is frequent in breast cancer patients undergoing radiotherapy (RT). Spectrophotometry (SP) is an objective and reliable tool for assessing RID severity. Despite intensive research efforts during the past decades, no sustainable prophylactic and treatment strategies have been found. Estimation of new and reevaluation of established risk factors leading to severe RID is therefore of major importance. Methods: 142 early breast cancer patients underwent whole-breast irradiation following breast-conserving surgery. RID was evaluated by physician-assessed Common Terminology Criteria of Adverse Events (CTCAE v4.03). Spectrophotometers provided additional semi quantification of RID using the L*a*b color-space. A total of 24 patient- and treatment-related parameters as well as subjective patient-assessed symptoms were analyzed. Results: Values for a*max strongly correlated with the assessment of RID severity by physicians. Breast volume, initial darker skin, boost administration, and treatment technique were identified as risk factors for severe RID. RID severity positively correlated with the patients’ perception of pain, burning, and reduction of everyday activities. Conclusions: Physician-assessed RID gradings correlate with objective SP skin measurements. Treatment technique and high breast volumes were identified as objective and significant predictors of RID. Our data provide a solid benchmark for future studies on RID with objective SP.

Purpose: Radiation dermatitis (RD) represents one of the most frequent side effects in radiotherapy (RT). Despite technical progress, mild and moderate RD still affects major subsets of patients and identification and management of patients with a high risk of severe RD is essential. We sought to characterize surveillance and nonpharmaceutical preventive management of RD in German-speaking hospitals and private centers. Methods: We conducted a survey on RD among German-speaking radiation oncologists inquiring for their evaluation of risk factors, assessment methods, and nonpharmaceutical preventive management of RD. Results: A total of 244 health professionals from public and private institutions in Germany, Austria, and Switzerland participated in the survey. RT-dependent factors were deemed most relevant for RD onset followed by lifestyle factors, emphasizing the impact of treatment conceptualization and patient education. While a broad majority of 92.8% assess RD at least once during RT, 59.0% of participants report RD at least partially arbitrarily and 17.4% stated to classify RD severity solely arbitrarily. 83.7% of all participants were unaware of patient-reported outcomes (PROs). Consensus exists on some lifestyle recommendations like avoidance of sun exposure (98.7%), hot baths (95.1%), and mechanical irritation (91.8%) under RT, while deodorant use (63.4% not at all, 22.1% with restrictions) or application of skin lotion (15.1% disapproval) remain controversial and are not recommended by guidelines or evidence-based practices. Conclusion: Identification of patients at an increased risk of RD and subsequent implementation of adequate preventive measures remain relevant and challenging aspects of clinical routines. Consensus exists on several risk factors and nonpharmaceutical prevention recommendations, while RT-dependent risk factors, e.g., the fractionation scheme, or hygienic measures like deodorant use remain controversial. Surveillance is widely lacking methodology and objectivity. Intensifying outreach in the radiation oncology community is needed to improve practice patterns.

Light sheet fluorescence microscopy (LSFM) is a transformative imaging method that enables the visualization of non-dissected specimen in real-time 3D. Optical clearing of tissues is essential for LSFM, typically employing toxic solvents. Here, we test the applicability of a non-hazardous alternative, ethyl cinnamate (ECi). We comprehensively characterized autofluorescence (AF) spectra in diverse murine tissues—ocular globe, knee, and liver—employing LSFM under various excitation wavelengths (405–785 nm) to test the feasibility of unstained samples for diagnostic purposes, in particular regarding percutaneous biopsies, as they constitute to most harvested type of tissue sample in clinical routine. Ocular globe structures were best discerned with 640 nm excitation. Knee tissue showed complex variation in AF spectra variation influenced by tissue depth and structure. Liver exhibited a unique AF pattern, likely linked to vasculature. Hepatic tissue samples were used to demonstrate the compatibility of our protocol for antibody staining. Furthermore, we employed machine learning to augment raw images and segment liver structures based on AF spectra. Radiologists rated representative samples transferred to the clinical assessment software. Learning-generated images scored highest in quality. Additionally, we investigated an actual murine biopsy. Our study pioneers the application of AF spectra for tissue characterization and diagnostic potential of optically cleared unstained percutaneous biopsies, contributing to the clinical translation of LSFM.

Relaying complex anatomical information between clinicians and to patients is challenging in medical routine and might be improved via direct 3D visualization of image series in augmented reality (AR) in portable devices. We devised a workflow for displaying medical imaging data in augmented reality on smartphones, relying solely on open-source products: ‘Medical Imaging XR’ (MIXR) application, ‘3D Slicer’ software, and ‘Fiji’. We designed a pipeline for image transformation and data harmonization (VR-prep) to expedite and optimize the 3D rendering of DICOM series in MIXR. VR-prep significantly reduced the size of image files to be uploaded to MIXR, without sacrificing quality and readability of images. Due to reduced file size, upload and download times onto the mobile device were significantly lower. Our workflow is suitable for CT, MRI, US and PET-CT, whereas MIXR alone only permits CT data. VR-prep is user-friendly and allows for broader integration of medical imaging data sets in AR, to facilitate clinician to clinician and clinician to patient communication. VR-prep relies exclusively in open-source software making it viable to adopt in a broad range of applications from clinical decision-making, patient communication, and medical education.